NumPy

npcpy is a Python library for research and development with multimodal language models, agentic AI, and knowledge graphs. It provides primitives for defining personas, making direct language-model calls, creating tool-using agents and multi-agent teams, and building AI applications with local providers such as Ollama, llama.cpp, omlx, and LM Studio as well as cloud providers. Its NPC Context-Agent-Tool data layer is designed to enforce context and tool-use rules through software rather than prompts. The Agent class includes tools such as shell execution, Python, file editing, and web search, while ToolAgent supports custom tools; the examples include image generation, Hugging Face image-dataset retrieval, and diffusion-model fine-tuning. The library is distributed through PyPI.

pandas is an open-source Python package for working with relational and labeled data through mutable DataFrame and related data structures. It supports missing-data handling, automatic or explicit label alignment, group-by split-apply-combine operations, indexing and slicing, merging and joining, reshaping and pivoting, hierarchical labels, and time-series operations such as date-range generation, frequency conversion, and rolling-window statistics. Its I/O tools load data from CSV and other delimited files, Excel, and databases, and support saving to and loading from HDF5; it is distributed through PyPI and Conda.

mjbatch is a Python library for running thousands of MuJoCo physics simulations in parallel on CPUs. It uses a C++ thread pool with the Python GIL released, provides live array access to simulation states and controls across a batch through bindings to MjData fields, and supports per-simulation model parameters with expansion of MjModel fields and recomputation of derived constants. The library includes examples for reinforcement learning, model-predictive control, system identification, and hardware co-design, including robot controllers, predictive-sampling control, parameter fitting, and joint optimization of robot properties and controls. It is distributed under the Apache-2.0 license.

NVIDIA NeMo Data Designer is an open-source framework for generating synthetic datasets from scratch or from seed data. It builds datasets column by column using sampler columns, LLM-generated text or structured columns, image and other multimodal models, existing seed datasets, and dependency-aware field generation. Workflows can connect to local or remote MCP servers to use external tools and capture interaction traces. Outputs can be previewed, resumed, and monitored, then validated or scored with Python, SQL, custom validators, and LLM judges. The framework also supports plugins for custom columns, seed readers, and processors, and includes a command-line interface for configuring model providers and models. It is distributed as the `data-designer` Python package and its source repository is licensed under Apache License 2.0. The library includes optional aggregate telemetry for model names and token counts, which can be disabled with `NEMO_TELEMETRY_ENABLED=false`.

Pillow is an open-source Python Imaging Library that adds image-processing capabilities to Python. It provides a documented API for loading, editing, resizing, normalizing, and saving images in many file formats, and is used in multimodal preprocessing pipelines. Pillow is a maintained fork and successor of the original Python Imaging Library (PIL), with source code distributed through GitHub and packages published to the Python Package Index.